Import iris data using sklearn library . Compute mean, mode, median, standard deviation, confidence interval and standard error for each feature ii. Compute correlation coefficients between each pair of features and plot heatmap iii. Find covariance between length of sepal and petal iv. Build contingency table for class feature

Import iris data using sklearn library or (Download IRIS data from:
(https://archive.ics.uci.edu/ml/datasets/iris or import it from sklearn.datasets)
i. Compute mean, mode, median, standard deviation, confidence interval and
standard error for each feature
ii. Compute correlation coefficients between each pair of features and plot heatmap
iii. Find covariance between length of sepal and petal
iv. Build contingency table for class feature



CODE


 import numpy as np

import pandas as pd

from sklearn.datasets import load_iris

from scipy import stats

import seaborn as sns

import matplotlib.pyplot as plt


# Load the Iris dataset

iris = load_iris()

data = iris.data

feature_names = iris.feature_names

target = iris.target

target_names = iris.target_names


# Convert data to a pandas DataFrame for easier analysis

df = pd.DataFrame(data, columns=feature_names)

df['class'] = target_names[target]


# Compute mean, mode, median, standard deviation, confidence interval, and standard error for each feature

statistics = {

    'mean': df.mean(),

    'mode': df.mode().iloc[0],

    'median': df.median(),

    'std_dev': df.std(),

    'confidence_interval': [stats.norm.interval(0.95, loc=mean, scale=std_dev / np.sqrt(len(df))) for mean, std_dev in zip(df.mean(), df.std())],

    'std_error': df.sem()

}


# Display statistics for each feature

for feature in feature_names:

    print(f"Statistics for {feature}:")

    print(f"Mean: {statistics['mean'][feature]}")

    print(f"Mode: {statistics['mode'][feature]}")

    print(f"Median: {statistics['median'][feature]}")

    print(f"Standard Deviation: {statistics['std_dev'][feature]}")

    print(f"Confidence Interval (95%): {statistics['confidence_interval'][feature]}")

    print(f"Standard Error: {statistics['std_error'][feature]}")

    print()


# Compute correlation coefficients between each pair of features

correlation_matrix = df.corr()


# Plot heatmap of correlation coefficients

plt.figure(figsize=(8, 6))

sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=".2f", linewidths=0.5)

plt.title("Correlation Coefficients Heatmap")

plt.show()


# Find covariance between length of sepal and petal

covariance = np.cov(df['sepal length (cm)'], df['petal length (cm)'])[0, 1]

print(f"Covariance between sepal length and petal length: {covariance}")


# Build contingency table for class feature

contingency_table = pd.crosstab(index=df['class'], columns='count')

print("Contingency Table:")

print(contingency_table)


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